Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Weighted linear cue combination with possibly correlated error.

Ipek Oruç1, Laurence T Maloney, Michael S Landy

  • 1Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA. ipek@cns.nyu.edu

Vision Research
|September 16, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dissociating the behavioral and computational features of implicit motor learning and explicit perturbation detection.

bioRxiv : the preprint server for biology·2026
Same author

Normalization accounts for temporal dynamics in human somatosensory cortex.

bioRxiv : the preprint server for biology·2026
Same author

Visual confidence accurately tracks increasing internal noise with eccentricity in peripheral vision.

bioRxiv : the preprint server for biology·2026
Same author

Implicit adaptation's effect on sensorimotor and motor confidence.

bioRxiv : the preprint server for biology·2025
Same author

The blur paradox: Better recognition at a distance.

Journal of vision·2025
Same author

Sensorimotor confidence during explicit motor adaptation.

bioRxiv : the preprint server for biology·2025
Same journal

Editorial for VSI Amblyopia: Advances in Amblyopia Research.

Vision research·2026
Same journal

Computational and mathematical models in vision: Quantitative approaches to understanding visual perception.

Vision research·2026
Same journal

Complex interactions between lightness, chroma, and hue in color ensemble perception.

Vision research·2026
Same journal

Driving with autism spectrum disorder: Exploring the impact of tactile hazard warnings on gaze behavior and hazard responses.

Vision research·2026
Same journal

Early visual processing in adults with ADHD: evidence from contrast sensitivity, spatial integration, and external noise.

Vision research·2026
Same journal

Pupil reflexes generate the peripheral drift illusion due to ON/OFF motion responses.

Vision research·2026
See all related articles

Human observers combine visual cues for slant estimation, with most performing better using combined linear perspective and texture gradient cues than single cues alone. Results suggest linear cue combination, with variations in optimality.

Area of Science:

  • Visual perception
  • Human psychophysics
  • Computational neuroscience

Background:

  • Human visual perception relies on integrating multiple cues to accurately estimate 3D scene properties like surface slant.
  • Understanding how the brain combines these cues is crucial for explaining visual performance and developing artificial systems.

Purpose of the Study:

  • To investigate how humans combine linear perspective and texture gradient cues for slant estimation.
  • To determine if cue combination follows optimal or sub-optimal models.

Main Methods:

  • Participants repeatedly estimated the slant of a plane (target: 75 degrees) using either linear perspective, texture gradient, or both.
  • Varied cue reliability (high/low variance) and measured setting variability across single-cue and combined-cue conditions.

Related Experiment Videos

  • Compared empirical performance in combined-cue conditions to predictions from single-cue performance.

    • Main Results:

    • Most observers (6/8) showed improved performance with combined cues compared to single cues.
    • Results were generally consistent with a linear combination model of cue integration.
    • Three observers demonstrated optimal combination of uncorrelated cues, while three others showed optimal combination with correlated internal estimates. Two observers exhibited sub-optimal cue combination.

    Conclusions:

    • Human slant estimation effectively integrates multiple visual cues, primarily through a linear combination process.
    • Individual differences exist in cue combination strategies, ranging from optimal to sub-optimal integration.
    • The findings provide insights into the neural mechanisms underlying multi-cue integration in the human visual system.